Network performance monitoring is an important issue in the networking community. Accurate and efficient estimation of the network performance provides fundamental supports for network management routines like traffic engineering and network anomaly detection.
Along the road of developing accurate and efficient network monitoring approaches, network tomography is noted for its ability to ascertain internal network performances from end-to-end measurements. Given the fast transient nature of Internet traffic dynamics, it is essential for network tomography to ascertain certain accuracy with a constrained time and traffic volume budget. However, when monitoring large scale networks, this is very challenging as on one side, low probing traffic volume is required to minimally affect underlying network performance, and on the other side, enough probing traffic is required to cover the entire network and achieve accurate estimation in a relatively short period.
In network tomography, multiple monitoring hosts are deployed at the edge of the network. These monitoring hosts function as receivers of end-to-end probing packets sent from a common source. By collecting delay and loss information at these monitoring hosts, network tomography develops maximum likelihood estimators (MLE) of delay and loss performances of internal network links on the paths from the source to the monitoring hosts. This ability makes network tomography especially appealing to applications that concern network performances but don't have access to internal network states. In particular, unicast based network delay tomography can be used to estimate network delay characteristics by probing a set of end host pairs in sequence.